Bibliographic record
Abstract
The expanding application of Concept Mapping includes its role in knowledge elicitation, institutional memory preservation, and ideation. With the advent of the CmapTools knowledge modeling software kit, Concept Mapping is being applied with increased frequency and success to address a variety of problems in the workplace. Supported by business application case studies, Applied Concept Mapping: Capturing, Analyzing, and Organizing Knowledge offers an accessible introduction to the theory, methods, and application of Concept Mapping in business and government. The case studies illustrate applications across a range of industriesincluding engineering, product development, defense, and healthcare. The authors provide access to a free download of CmapTools, courtesy of the Institute for Human and Machine Cognition, to enable readers to create and share their own Concept Maps. Offering examples from the United States, Canada, Australia, Spain, Brazil, Scotland, and The Netherlands, they highlight a global perspective of this dynamic tool. The text is organized into three sections: Practitioners Viewssupplies narratives, guidance, and reviews of applications from career Concept Mappers Recent Case Studies and Resultspresents in-depth examinations of specific applications and their results Pushing the Boundariesexplores whats possible and where the boundary conditions lie Applied Concept Mapping facilitates the fundamental understanding needed to harness the power of Concept Mapping to develop viable solutions to a virtually unlimited number of real-world problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.093 | 0.027 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".